Computational Approaches to Identify Promoters and cis-Regulatory Elements in Plant Genomes

Abstract

The identification of promoters and their regulatory elements is one of the major challenges in bioinformatics and integrates comparative, structural, and functional genomics. Many different approaches have been developed to detect conserved motifs in a set of genes that are either coregulated or orthologous. However, although recent approaches seem promising, in general, unambiguous identification of regulatory elements is not straightforward. The delineation of promoters is even harder, due to its complex nature, and in silico promoter prediction is still in its infancy. Here, we review the different approaches that have been developed for identifying promoters and their regulatory elements. We discuss the detection of cis-acting regulatory elements using word-counting or probabilistic methods (so-called "search by signal" methods) and the delineation of promoters by considering both sequence content and structural features ("search by content" methods). As an example of search by content, we explored in greater detail the association of promoters with CpG islands. However, due to differences in sequence content, the parameters used to detect CpG islands in humans and other vertebrates cannot be used for plants. Therefore, a preliminary attempt was made to define parameters that could possibly define CpG and CpNpG islands in Arabidopsis, by exploring the compositional landscape around the transcriptional start site. To this end, a data set of more than 5,000 gene sequences was built, including the promoter region, the 5Ј-untranslated region, and the first introns and coding exons. Preliminary analysis shows that promoter location based on the detection of potential CpG/CpNpG islands in the Arabidopsis genome is not straightforward. Nevertheless, because the landscape of CpG/ CpNpG islands differs considerably between promoters and introns on the one side and exons (whether coding or not) on the other, more sophisticated approaches can probably be developed for the successful detection of "putative" CpG and CpNpG islands in plants.Arabidopsis, and probably most plants, encode an exceptionally large number of DNA-binding proteins, potentially acting as transcription factors (TFs). In fact, more than 3,000 genes have been anticipated to be involved in transcription, more than one-half of which were expected to encode TFs (Arabidopsis Genome Initiative, 2000), corresponding to more than 5% of the Arabidopsis genes, and approximately twice the ratio observed for yeast and animal genomes (Riechmann et al., 2000). These TFs bind to the DNA on specific cis-acting regulatory elements (CAREs) and orchestrate the initiation of transcription, which is one of the most important control points in the regulation of gene expression. CAREs are short conserved motifs of five up to 20 nucleotides usually found in the vicinity of the 5Ј end of genes in what is called the promoter. The promoter sequence is usually located upstream from the transcription start site (TSS), but regulatory elements can also be located downstream, for ...

Detoxification and Transcriptome Response in Arabidopsis Seedlings Exposed to the Allelochemical Benzoxazolin-2(3H)-one

“…An important caveat concerning data generated by promoter motif discovery algorithms is that the functionality of an over-represented motif must be determined experimentally and that motif detection in silico can only provide likely candidates for such follow-up studies (30). In addition, it is possible that motifs identified in the present study are functional but not associated with physiological processes related to phytotoxin exposure.…”

“…All searches were performed using a precompiled 3rd order Markov background model based on Arabidopsis upstream sequences (29), prior probability of finding 1 motif instance ϭ 0.5, maximum number of motif instances per sequence ϭ 0 (no limit), and maximum allowed overlap between different motifs ϭ 2. Each data set was analyzed 10 times using the same parameters to reduce local optima (30), and only cases where an identical or similar consensus sequence was returned from multiple runs were further considered. In addition, two independent statistical tests were employed for motif validation.…”

Classificadores e Multi-classificadores para a Predição de Promotores em Bacillus subtilis

“…Apesar dessas limitações, um grande número de programas de predição de promotores tem sido desenvolvido para organismos eucariotos [5], [6], [7], [8]. Entretanto, até agora, são poucos os sistemas que podem ser usados como ferramenta para a predição de promotores em organismos procarióticos, como o programa Neural Network Promoter Prediction (NNPP).…”

In Silico Identification of Regulatory Elements in Promoters

“…Orthologous noncoding DNA sequences from multiple species provide a strong base for identification of regulatory elements by Phylogenetic footprinting ( Fig. 1) (Rombauts et al, 2003). The major advantage of phylogenetic footprinting over the single genome is that multigene a p p r o a c h r e q u i r e s d a t a o f c o r e g u l a t e d g e nes.…”